EpiPlanAgent: Agentic Automated Epidemic Response Planning
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system using large language models (LLMs) to automate the generation and validation of digital emergency response plans. The multi-agent framework integrated task decomposition, knowledge grounding, and simulation modules. Public health professionals tested the system using real-world outbreak scenarios in a controlled evaluation. Results demonstrated that EpiPlanAgent significantly improved the completeness and guideline alignment of plans while drastically reducing development time compared to manual workflows. Expert evaluation confirmed high consistency between AI-generated and human-authored content. User feedback indicated strong perceived utility. In conclusion, EpiPlanAgent provides an effective, scalable solution for intelligent epidemic response planning, demonstrating the potential of agentic AI to transform public health preparedness.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 4 of its 22 references that are in the map. First posted on arXiv 11 Dec 2025.
Builds on
4 of 22 references.
References · 4
- 2025Agentic Large Language Models, a surveycited by 105
- 2025Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoningcited by 19
- 2025EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecastingcited by 5
- 2025Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resiliencecited by 4
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- 2026Driving Epidemic Models with AI Agents: the Epydemix Agent Framework
- 2026EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models
- 2026The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models
- 2026EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections
- 2026ARIES: A Scalable Multi-Agent Orchestration Framework for Real-Time Epidemiological Surveillance and Outbreak Monitoring
- 2025What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness